Methods and apparatus for determining audience metrics across different media platforms

ABSTRACT

An example includes a segment collector to: access impression records indicative of media access segments, the media access segments including start times and end times corresponding to media accessed by a panelist; and determine ones of the impression records that include a watermark corresponding to a first media platform presenting the media; a segment classifier to convert a first one of the impression records including the watermark to a converted impression record; and combine the converted impression record corresponding to the first media platform and a second impression record corresponding to a second media platform; and a media creditor to generate audience measurement metrics based on the combined impression records.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 15/299,055 (now U.S. Pat. No. 10,412,469), which was filed on Oct.20, 2016, which claims benefit of Indian Patent Application Serial No.4149/DEL/2015, which was filed in the Indian Patent Office on Dec. 17,2015. U.S. patent application Ser. No. 15/299,055 and Indian PatentApplication Serial No. 4149/DEL/2015 are hereby incorporated herein byreference in their entireties. Priority to U.S. patent application Ser.No. 15/299,055 and Indian Patent Application Serial No. 4149/DEL/2015 isclaimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement and, moregenerally, to methods and apparatus for determining audience metricsacross different media platforms.

BACKGROUND

While in the past, audio and/or audio-visual media was primarilyaccessed via free, terrestrial broadcast of television or radio media,media may now be accessed in many different ways. For instance, cableand satellite broadcast services provide access to a large variety ofchannels of television, movie and radio media, typically on asubscription basis. In addition, such services also often include avideo-on-demand component, allowing consumers to access media (usuallyfor a fee) whenever they wish.

The rise in popularity of the Internet has further diversified the mediadelivery ecosystem, providing many new ways to access media (e.g.,television, movies, radio, webpages, etc.). For example, Internet basedservices from entities such as Amazon, Netflix, Roku and/or othersenable users to stream movies and television programs at any time. Somesuch services do not require subscription to a cable or satelliteprovider, and are sometimes referred to as over the top (OTT) services.Moreover, whereas traditional television and radio broadcasts wereprimarily presented at the time of receipt and, thus, viewed in a timelinear fashion, Internet and other technologies have enabled media to bewatched in a non-linear fashion. In particular, media from OTT and otherplatforms enable the presentation of media to be stopped, paused,rewound, fast forwarded and/or otherwise time shifted. Thus, theconsumer can access Internet distributed media in a non-linear fashionfrom any of a variety of different sources.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example media measurement system shownin an example environment of use and including a watermark based mediaimpression handler constructed in accordance with the teachings of thisdisclosure.

FIG. 2 is a block diagram of an example implementation of the exampleview counter of FIG. 1

FIG. 3 illustrates an example first use case for a full view of livemedia.

FIG. 4 illustrates an example second use case for pausing of live media.

FIG. 5 illustrates an example third use case for breaking viewthreshold.

FIG. 6 illustrates an example fourth use case for watching on delay.

FIG. 7 illustrates an example fifth use case for fast-forward to live.

FIG. 8 illustrates an example sixth use case for live versus DVR.

FIG. 9 illustrates an example seventh use case for live versus DVR.

FIG. 10 illustrates an example eighth use case for live versus DVR.

FIGS. 11a-11b illustrates an example ninth use case for changingchannels.

FIG. 12 illustrates an example tenth use case for day boundary crossing.

FIG. 13 is a graph of an example of total viewing starts (Live, DVR,VOD) across all mapped programs and episodes (pgm/epsds) for a specificdate (live/same day viewing only).

FIG. 14 is a graph of example differences in viewing starts from a priorInterval.

FIG. 15 is a graph of an example of a percent change in viewing startsfrom a prior Interval.

FIG. 16 is a flowchart representative of example machine readableinstructions which may be executed to generate watermark basedimpression records.

FIG. 17 is a flowchart representative of example machine readableinstructions which may be executed to generate Internet based impressionrecords.

FIG. 18 is a flowchart representative of example machine readableinstructions which may be executed to compare a non-Internet basedaudience to an Internet based audience.

FIG. 19 is a flowchart representative of example machine readableinstructions which may be executed to implement block 1845 of FIG. 18 toconvert watermark based impression records to Internet-based mediacompatible impression records.

FIG. 20 is a block diagram of an example processor platform which mayexecute the example instructions of FIGS. 18-19, to implement theexample watermark based media impression handler of FIG. 1 and/or theexample view counter of FIGS. 1 and/or 2.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

While in the past, audio and/or audio-visual media was primarilyaccessed via free, terrestrial broadcast of television or radio media,media may now be accessed in many different ways. For instance, cableand satellite broadcast services provide access to a large variety ofchannels of television, movies and radio media, typically on asubscription basis. In addition, such services also often include avideo on demand (VOD) component, allowing consumers to access media(usually for a fee) whenever they wish.

The rise in popularity of the Internet has further diversified the mediadelivery ecosystem, providing many new ways to access media (e.g.,television, movies, radio, webpages, etc.). For example, Internet basedservices from entities such as Amazon, Netflix, Roku and/or othersenable users to stream movies and television programs at any time. Somesuch services do not require subscription to a cable or satelliteprovider, and are sometimes referred to as over the top (OTT) services.Moreover, whereas traditional television and radio broadcasts wereprimarily presented at the time of receipt and, thus, viewed in a timelinear fashion, Internet and other technologies have enabled media to bewatched in a non-linear fashion. In particular, media from OTT and otherplatforms enable the presentation of media to be stopped, paused,rewound, fast forwarded and/or otherwise time shifted. Thus, theconsumer can access Internet distributed media in a non-linear fashionfrom any of a variety of different sources.

While this multiplication of media access opportunities and control overthe media access experience has benefited consumers, it has brought manychallenges to the audience measurement industry. The audiencemeasurement industry, led by the Nielsen Company, seeks to accuratelydetermine the size and demographic composition of the audience ofvarious media. Traditionally when media was primarily accessed via thefree terrestrial broadcast model, there were far fewer sources tomeasure and non-linear viewing was not an issue. In the new eco-system,it is desirable to measure media exposure across all access models. Forexample, it is desirable to measure the free terrestrial broadcastaudience and the Internet based audience of the same television programto have a complete picture of the demographics and numbers of peopleexposed to that program.

Traditionally, exposure to television media (content and/oradvertisements) has been measured by panel based systems. A “panel,” asused herein, is a group of persons who have agreed to have, for example,their media access habits monitored by an audience measurement company.Each person participating in the panel is called a “panel member.” Suchpanel members register to participate in the panel by agreeing to havetheir media exposure habits monitored and by grouping their demographicdata. As an example, the commonly referred to “Nielsen family” is ahousehold of panelists that has agreed to have their media usage habitsmonitored by the Nielsen Company.

Often, the media measurement company conducting such a study installsvarious electronics in the panelist home to automatically collect dataidentifying media and/or media exposure habits and return that collecteddata to a centralized facility at the audience measurement entity foraggregation with data from other panelists. Sometimes panelists aremonitored with portable meters which are intended to be carried by thepanelists. Such meters, which may be implemented by software executingon a cellular telephone or by a specially designed device (e.g., aportable people meter), are useful as they collect data representingboth in home and out of home media exposure because the meter travelswith the panelist throughout the day.

Media identification by such electronics has been carried out in manyways over the years. For example, utilizing the Nielsen Company'sproprietary Active/Passive (AP) model, television and radio mediabroadcasters have encoded their media with codes (sometimes referred toas watermarks). These codes are encoded into the media in apsychoacoustic manner such that the codes can be detected by electronics(e.g., by the meter in a panelist household or a portable meter), buttypically not heard by the human ear. The codes used by the Nielsencompany are embedded at a high periodicity (e.g., every two seconds) andinclude a broadcaster identifier and a timestamp. Because the timestampsare highly granular, a computer (e.g., a server at the centralfacility), can quickly identify the portion of the media exposed to thepanelist by comparing the watermark(s) collected by a meter located at apanelist home (or, in the case of a portable meter, carried by apanelist) against a table of reference watermarks mapping broadcasteridentifiers and timestamps to media.

For a variety of reasons, Internet based media do not have the benefitof such watermarking. As an example, watermarks used in the radio ortelevision broadcasting context, might not survive the compressionand/or encryption techniques used in Internet-based media distribution.Thus, meters are provided with one or more of a variety of othertechniques to identify media exposure to Internet distributed media.

As mentioned above, it is desirable to determine the audience of a givenpiece of media across all delivery mediums (e.g., terrestrial broadcast,cable, satellite, Internet, etc.). It is also desirable to compare theaudiences of different delivery mediums in size and/or demographics toenable comparison of different media providers. To do this, it isdesirable to employ a consistent set of metrics across the various mediadelivery platforms. Two such important base metrics are “UniqueAudience” and “Duration Viewed.” As used herein, “Unique Audience”refers to an unduplicated count of persons. In other words, the sameperson is counted only once in an audience. As used herein, “DurationViewed”, refers to the total amount of time persons viewed the media inquestion. Currently, the Unique Audience and Duration Viewed metricshave the same meaning in the traditional television measurement contextand the Internet-based media measurement context.

A third basic measurement, namely, “views (Impressions)” as used hereinrefers to the count of total views. The views metric is additive, in thesense that the same person can be counted multiple times (e.g., forviewing the same media or portion(s) of the same media twice). Prior tonow, the views metric has not had a consistent meaning in both thetraditional television context and the Internet-based media measurementcontext. Indeed, it has primarily been an Internet-based mediameasurement metric. In the Internet-based media measurement context, theview metric is based on a view start, and indicates the number of timesthe media (e.g., a video) began playing. In other words, it is a countof play initiation events. Because of the control over presentationprovided to the consumer in the Internet delivery context, the samemedia may be started and stopped multiple times. Thus, in theInternet-based media measurement context, play initiation events havebeen counted as separate views (impressions) if separated by a timethreshold, such as one second.

The “view” metric in Television Ratings has not previously explicitlyexisted. Because, for example, of the granularity provided by watermarksin the television and radio contexts as explained above, it is possibleto identify with precision the minutes of a given piece of media towhich a panelist was exposed. Therefore, television ratings definesmultiple views as a person seeing the same minute of the same media(minute of media is referenced as “MOP”) multiple times. Therefore, if aperson watched a television program and the minute they saw the mostnumber of times was seen X times, then they are considered to haveviewed the video X times in the television context. A similar MOPs basedapproach cannot be applied to the Internet based media circumstancebecause, for example, the granularity of media exposure trackingprovided by watermarks is typically not present in the Internet context.

Therefore, to overcome the problems created by the differenttechnologies of television, radio and Internet-based media deliverysystems, examples disclosed herein will apply a new views metric in thetelevision context. This metric, namely, a “view start,” will be appliedto determine when two viewing segments (e.g., exposure to two segmentsof the same video broadcast) are separate views or will be merged intoone view based on a viewing time threshold.

FIG. 1 is a block diagram of an example media impression handling system100, constructed in accordance with the teachings of this disclosure fordetermining audience metrics across different media platforms and shownis an example environment of use.

In the illustrated example of FIG. 1, an example cross-platform mediaratings environment includes one or more example media presentationenvironments 102 a-102 n (where n represents any integer), such as roomsof a household (e.g., a room in a home of a panelist, such as the homeof a “Nielsen family”), a mobile environment, a restaurant, a tavern, ora retail location. In the one or more example media presentationenvironments 102 a-102 n are one or more panel members or panelists 101a-101 n (where n represents any integer), one or more example mediapresentation devices 105 a-105 n access media from one or more mediaproviders 120A-120N via one or more different mediums (e.g., Internet,terrestrial RF broadcast, etc.). In the example of FIG. 1, the examplemedia presentation environment 102 a receives media distributed via theInternet (e.g., streaming media) and the example media presentationenvironment 102 b receives over the air broadcast via the terrestrialbroadcast system 120 a. The example media presentation environments 102a-102 n are provided with meters 108 a-108 n to identify the mediapresented by the media presentation devices 105 a-105 n and report mediamonitoring information to an example central facility 104 of an exampleaudience measurement entity via an example network 110.

In the illustrated example of FIG. 1, the example media presentationdevices 105 a, 105 b (e.g., a display device or television), receivemedia from one or more media providers 120A-120N. In FIG. 1, the examplemedia presentation environment 102 a is adapted to access media via thenetwork 110 (e.g., an Internet-based home), whereas the example mediapresentation environment 102 b is adapted to receive media via the RFbroadcast tower 120A (e.g., a terrestrial TV-based home).

The media providers 120A-120N may be any type of media provider(s), suchas, but not limited to, a cable media service provider, a radiofrequency (RF) media provider, an Internet based provider (e.g., IPTV),a satellite media service provider, etc., and/or any combinationthereof. As used herein, media refers to content and/or advertisements.The media may be radio media, television media, pay per view media,movies, Internet Protocol Television (IPTV) media, satellite television(TV) media, digital television media, stored media (e.g., media on acompact disk (CD), a Digital Versatile Disk (DVD), a Blu-ray disk,etc.), audio media and/or video media directed (e.g., streamed) via theInternet, a video game, targeted broadcast media, satellite broadcastmedia, video on demand media, and/or any other type(s) of broadcast,multicast and/or unicast media. For example, the media presentationdevice 110 may be implemented by a television and/or display device thatsupports the National Television Standards Committee (NTSC) standard,the Phase Alternating Line (PAL) standard, the Système Électronique pourCouleur avec Mémoire (SECAM) standard, a standard developed by theAdvanced Television Systems Committee (ATSC), such as high definitiontelevision (HDTV), a standard developed by the Digital VideoBroadcasting (DVB) Project, etc. Advertising, such as an advertisement(e.g., to spur sales of a product or service) and/or a preview of otherprogramming that is or will be offered by the media provider(s)120A-120N, etc., is often interleaved with content in the media.

In examples disclosed herein, an audience measurement entity (AME)provides the meter (e.g., 108 a) to the panelist (e.g., 101 a). The AMEconfigures the meter 108 a to detect the panelist's 101 a exposure tomedia and to electronically store and/or transmit monitoring informationto a central facility 104. The monitoring information may be a codedetected from the presented media, a signature of the presented media,an identifier of a panelist present at the time of the presentation, atimestamp of the time of the presentation and/or information derivedfrom the monitoring information (e.g., viewing segment information,Viewing Classification information, view start information, etc.).

In the illustrated example, the media monitoring information collectedby the meter 108 a is transmitted (e.g., periodically or aperiodically)to the example central facility 104 via a gateway 114 a through theexample network 110. While the media monitoring information istransmitted to the central facility 104 by electronic transmission inthe illustrated example of FIG. 1 (e.g., transmitted at a fixedinterval, random interval, pseudo-random interval, upon request orpolling by the central facility, etc.), the media monitoring informationmay additionally or alternatively be transferred in any other mannersuch as, for example, by physically mailing the meter 108 a, byphysically mailing a data store 116A or memory of the meter 108 a, etc.

The network 110 of the illustrated example in FIG. 1 is a wide areanetwork (WAN) such as the Internet. However, in some examples, localnetworks may additionally or alternatively be used. Moreover, theexample network 110 may be implemented using any type of public orprivate network such as, but not limited to, the Internet, a telephonenetwork, a local area network (LAN), a cable network, and/or a wirelessnetwork, or any combination thereof.

In some examples, the gateway (e.g., 114 a, etc.) facilitates deliveryof media from the media provider(s) 120A-120N to the media presentationdevice (e.g., 105 a, etc.) via the Internet. In some examples, theexample gateway (e.g., 114 a) includes gateway functionality such asmodem capabilities. In some other examples, the example gateway (e.g.,114 a) is implemented in two or more devices (e.g., a router, a modem, aswitch, a firewall, etc.). In some examples, the gateway (e.g., 114 a)communicates with the network 110 via Ethernet, a digital subscriberline (DSL), a telephone line, a coaxial cable, a USB connection, aBluetooth connection, any wireless connection, etc. to access media fromone or more of the media providers 120A-120N. In some examples, theexample gateway (e.g., 114 a) hosts a Local Area Network (LAN) for themedia presentation environment (e.g., 102 a). In the illustrated exampleof FIG. 1, the example gateway 114 a is connected to a local network(e.g., a LAN), physically or wirelessly, allowing the meter 108 a, themedia presentation device 105 a and the gateway 114 a to exchange data.In some examples, the example gateway (e.g., 114 a) is implemented by acellular communication system and may, for example, enable the meter(e.g., 108 a) to transmit information to the central facility 104 usinga cellular connection.

The central facility 104 of the illustrated example is implemented byone or more servers or services. The central facility 104 of thisexample stores and processes data received from the meter(s) 108 a-108n.

As discussed above, the media delivery ecosystem is diverse and involvestraditional television and/or radio broadcast (e.g., represented byterrestrial media provider 120A) and Internet-based media providers(represented by media providers 120B-120N). The central facility 104 ofthis example is structured to process media monitoring information formedia distributed by the different systems in a manner that enablescounting and comparison of the same. For example, the central facility104 is able to determine a total audience for media distributed via theInternet and via traditional broadcast (e.g., television, etc.). To thisend, the central facility 104 of FIG. 1 includes an example watermarkbased media impression handler 150 to handle watermarked media (e.g.,non-Internet based media) and an example non-watermark based mediaimpression handler 151 to handle non-watermarked media (e.g., Internetbased media). In some examples, the example watermark based mediaimpression handler 150 and/or subparts thereof may be distributed in oneor more devices and/or one or more locations. In some examples, thecentral facility 104 generates report(s) for advertisers, programproducers and/or other interested parties based on the data receivedfrom the meter(s) 108 a-108 n.

In the example of FIG. 1, the watermark based media impression handler150 is structured to process media monitoring information collected bymeters (e.g., meter 108 b) monitoring media accessed via traditionalbroadcast (e.g., terrestrial RF television or radio, etc.) that enablesit to be compared to and/or combined with media monitoring informationcollected by meters (e.g., meter 108 a) monitoring media distributed viathe Internet (e.g., “streaming media”). In the illustrated example, themedia monitoring information collected for traditional broadcast mediaincludes code(s)/watermark(s) and, thus, provides a high level ofgranularity into the sections of media accessed at a particular site.The watermark based media impression handler 150 of the illustratedexample effectively normalizes this data to ensure views/impressions areattributed in a manner compatible with the media monitoring informationcollected for Internet-based media exposures. Therefore, the examplewatermark based media impression handler 150 of FIG. 1 solves theproblem created by the lack of codes/watermarks that occur in theInternet based distribution model (e.g., due to compression, filtering,stripping and/or correcting of codes in the distribution of theInternet-based media and/or due to Internet media provider failing toencode the media).

In the example of FIG. 1, the watermark based media impression handler150 includes a record locater 152, a view counter 154 and a mediacreditor 156. The record locater 152 of the illustrated exampleaccesses, in a data store or memory device (e.g., as shown in FIG. 2, alocal memory 2013, a volatile memory 2014, a non-volatile memory 2016,etc.), a first media impression record indicative of a first mediaaccess segment and a second media impression record indicative of asecond media access segment. The view counter 154 analyzes theimpression records accessed by the record locater 152 and, via a viewingsegment collector 213, viewing segment sorter 214, viewing segmentclassifier 215 and view start designator 216, provide a “view” metricthat facilitates, via the media creditor 156, comparison and/orcombination of the records across Internet based media distributionplatforms and non-Internet based media distribution platforms, such astraditional television or radio.

FIG. 2 is a block diagram illustrating an example implementation of theexample view counter 154 of FIG. 1. In the example implementation ofFIG. 1, the example view counter 154 includes an example viewing segmentcollector 213, an example viewing segment sorter 214, an example viewingsegment classifier 215 and an example view start designator 216. In someexamples, the view counter 154 and/or any of its components may bedistributed in one or more devices, in one or more locations, remotefrom the central facility 104, etc. For instance, one or more of theview counter 154, the example viewing segment collector 213, the exampleviewing segment sorter 214, the example viewing segment classifier 215and/or the example view start designator 216 may be integrated with theexample meters 108 a-108 n, with the balance of the component parts, ifany, implemented at the central facility 104 or at other systems ordevices (e.g., media presentation devices 105 a-105 n).

FIG. 2 shows an example view counter 154, which may be provided in wholeor in part in the central facility 104, as shown in FIG. 1, or, inalternative examples, in the meters 108 a-108 n, or, in one or moreother devices and/or locations. In general, the view counter 154processes media monitoring information (e.g., start time, viewing times,etc.) received from panelists and normalizes that data to facilitatecomparison and/or combination of the same across Internet based mediadistribution platforms and non-Internet based media distributionplatforms such as traditional television or radio. An exampleimplementation of the view counter 154 of FIGS. 1 and/or 2 isillustrated in FIGS. 18 and/or 19, which depict flowchartsrepresentative of example machine readable instructions which may beexecuted to implement the view counter 154 to convert watermark basedimpression records to Internet-based media compatible impression records(see block 1845 of FIG. 18; FIG. 19).

While some metrics are common to both traditional media distributionplatforms (e.g., television, radio, etc.) and Internet-based mediadistribution platforms, such as Unique Audience (e.g., the unduplicatedcount of persons during a reporting period) and Duration Viewed (e.g.,the amount of time total persons viewed during a reporting period),other metrics are disparate and are not able to be meaningfully comparedto one another. By way of example, the “view” metric in Internet-basedmedia distribution content indicates a number of times that specificmedia started playing. The example watermark based media impressionhandler 150 newly provides a “view” metric for conventional (i.e.,non-Internet based) media distribution platforms. In the illustrativeexamples, the example watermark based media impression handler 150aligns the “view” metric across both non-Internet based platforms andInternet based media distribution platforms by providing a new,non-Internet based media distribution platform view start metric, whichis combinable with the view start metric employed in Internet basedmedia distribution platform measurement, to advantageously enableapplication of a single set of metrics across non-Internet based andInternet based media distribution platforms. This, in turn, permitsdevelopment of a unified Total Content Ratings (TCR) and Total AudienceMeasurement that simultaneously satisfies the needs of both non-Internetbased and Internet based media distribution platforms and their clients.

The example view counter 154 of FIG. 2, whether collectively provided inthe example watermark based media impression handler 150, or distributedin different structures, processes media monitoring data fromnon-Internet based panelists to generate non-Internet based media viewstarts combinable with Internet based media view starts. As mentionedabove, non-Internet based media is often identifiable viawatermarks/code embedded in the media. This data particularly identifiestimes in the media presented with high granularity (e.g., every 2.5seconds of the media may be labeled with a time stamp). Internet basedmedia doesn't involve such watermarks. To make the data from thisdifferent platform comparable, the view counter 154 of the illustratedexample processes the non-Internet based data to determine a number ofview starts. To this end, the example view counter 154 of FIG. 2includes the viewing segment collector 213, viewing segment sorter 214,viewing segment classifier 215 and view start designator 216.

The example viewing segment collector 213 of the view counter 154collects all viewing segments (e.g., viewing events that come from anon-Internet based media meter monitoring broadcast media (e.g., TV,radio, etc.) of a specific piece of media for an identified panelist forthe relevant period (e.g., second(s), minute(s), hour(s), day(s), etc.)of measurement. The media may include content and/or advertisementsdelivered via any type of non-Internet based distribution medium. Toillustrate, FIG. 3 shows a single viewing segment 300, while FIG. 4shows three separate viewing segments of a program P1E1 (Program 1,Episode 1), with a first viewing segment 400 a from 6:00-6:10 pm, asecond viewing segment 400 b from 6:15 pm-6:30 pm and a third viewingsegment 400 c from 6:45 pm-7:20 pm. In the illustrated example, thecollected viewing segments or media access segments (e.g., 400 a-400 x,where x can be any integer) of the specific piece of media are sorted bythe viewing segment sorter 214 of the view counter 154 (e.g.,chronologically sorted, etc.) for a particular panelist for the relevantperiod. The output of the viewing segment collector 213 and/or of theviewing segment sorter 214 are then input to the viewing segmentclassifier 215 of the view counter 154 for determination as to whetherthe viewing segments (e.g., 400 a-400 c in the example of FIG. 4) are tobe combined into one view or identified as more than one view. Thisdetermination by the viewing segment classifier 215 is premised upon anamount of time between adjacent viewing segments wherein, if an amountof time between adjacent viewing segments (e.g., viewing occurrences ordata fields that occur sequentially or are separated by a time period)is less than and/or equal to a pre-defined view threshold (e.g., anyselected time period such as 1 second, 15 seconds, 30 seconds, 1 minute,5 minutes, 10 minutes, 15 minutes, 20 minutes, 60 minutes, 61 minutes,etc.), the adjacent viewing segments are considered to be a single view.

For instance, in the example of FIG. 4, wherein a first viewing segment400 a is separated from a second viewing segment 400 b by 5 minutes,which is less than the example view threshold of 20 minutes, and a thirdviewing segment 400 c is separated from the second viewing segment 400 bby 15 minutes, which is less than the example view threshold of 20minutes, the output of the viewing segment classifier 215 is a singleview 402 a. In other words, the viewing segment classifier 215 combinesthe first, second and third viewing segments 400 a, 400 b, 400 c intoone viewing segment, thus associating one video start with the threeviewing segments 400 a-400 c. Thus, multiple viewing segments may becombined into a single view, depending on the time period(s)therebetween.

The use case of FIG. 3 involves only one viewing segment 300, which isthen classified as one view 302.

FIG. 5 shows another example wherein a first viewing segment 500 a isseparated from a second viewing segment 500 b by 25 minutes, which isgreater than the example view threshold of 20 minutes, and a thirdviewing segment 500 c is separated from the second viewing segment 500 bby 25 minutes, which is greater than the example view threshold of 20minutes. In the example of FIG. 5, the viewing segment classifier 215classifies the first, second and third viewing segments 500 a-500 c asthree separate views 502 a-502 c.

Following a determination of the number of views by the viewing segmentclassifier 215, the view start designator 216 of the view counter 154attributes one view start, and resulting duration, to each classifiedview. Further, each view start assigned by the view start designator 216is further assigned to a specific type (e.g., Live, digital videorecorder (DVR) viewing, or video-on-demand (VOD)) based on one or morecharacteristics of the view(s) (e.g., a live view, a DVR view, etc.).This type determination is, in some examples, also dependent on one ormore weighting criteria. In some examples, the view start designator 216assigns a “live” source to views that are associated with a greaternumber of “live” minutes than “DVR” minutes. In another example, theview start designator 216 assigns a “DVR” source to views wherein thereis a greater number of DVR or time-shifted minutes than live minutes.

While example manners of implementing the watermark based mediaimpression handler 150 is depicted in FIG. 1 and of implementing theview counter 154 is illustrated in FIGS. 1 and/or 2, one or more of theelements, processes and/or devices illustrated in FIG. 2 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example watermark based media impressionhandler 150, the example record locator 152, the example view counter154, the example media creditor 156, the example viewing segmentcollector 213, the example viewing segment sorter 214, the exampleviewing segment classifier 215 and the example view start designator 216of FIGS. 1 and/or 2 may be implemented by hardware, software, firmwareand/or any combination of hardware, software and/or firmware. Thus, forexample, any of the example watermark based media impression handler150, the example record locator 152, the example view counter 154, theexample media creditor 156, the example viewing segment collector 213,the example viewing segment sorter 214, the example viewing segmentclassifier 215 and the example view start designator 216 of FIGS. 1and/or 2, or other examples expressly or implicitly disclosed herein,could be implemented by one or more analog or digital circuit(s), logiccircuits, programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)).

When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example watermark based media impression handler 150, the examplerecord locator 152, the example view counter 154, the example mediacreditor 156, the example viewing segment collector 213, the exampleviewing segment sorter 214, the example viewing segment classifier 215and the example view start designator 216 of FIGS. 1 and/or 2, or otherexamples expressly or implicitly disclosed herein, are hereby expresslydefined to include a tangible computer readable storage device orstorage disk such as a memory, a digital versatile disk (DVD), a compactdisk (CD), a Blu-ray disk, etc. storing the software and/or firmware.Further still, the example watermark based media impression handler 150,the example record locator 152, the example view counter 154, theexample media creditor 156, the example viewing segment collector 213,the example viewing segment sorter 214, the example viewing segmentclassifier 215 and the example view start designator 216 of FIGS. 1and/or 2, or other examples expressly or implicitly disclosed herein,may include one or more elements, processes and/or devices in additionto, or instead of, those illustrated in FIG. 2, and/or may include morethan one of any or all of the illustrated elements, processes anddevices.

Disclosed herein in FIGS. 3-12 are examples illustrating examples of a“view” metric (the “view” metric for the non-Internet based media isreferred to herein as a “non-Internet based media view start” or“non-Internet based media view start (“V.S.”)”). As noted above, thecreation of this “view” metric facilitates alignment of non-Internetbased media view starts to internet based media view starts to enabledevelopment of, for example, a Total Content Ratings (TCR) employing asingle set of metrics. FIGS. 3-12 further serve to illustrate treatmentsof multiple live and time-shifted categories (e.g., media recorded by adigital video recorder) video viewing segments or media access segmentsassociated with different source characteristics. Example sourcecategories include a live category (e.g., viewing determined to occursubstantially at the broadcast time) and a time-shifted category (e.g.,viewing media at a time after it is broadcast via DVR, PVR, or thelike). Although examples are described herein in the context ofmeasuring non-Internet based media viewing segments, it is to beunderstood that the disclosed examples can be applied to measure viewingsegments of media presented by any type(s), number(s), and/orcombination(s) of media devices.

FIG. 3 shows an example of a first use case for a full live view. Thisis a simple use case, wherein a person watched a program (program 1,episode 1, or “P1E1” in FIG. 3) live from beginning to end withoutinterruption from 6:00 pm-7:00 pm. The live viewing is also denoted bythe “Play Delay” of “0” below the beginning and ending of the P1E1viewing segment or media access segment 300 and the metric of “LiveMinutes=60.” The media access segment 300 itself includes, or isoperatively associated with, a duration during which a person isidentified as having accessed an instance of media (e.g., an episode, acommercial, a movie, a television show, a radio program, or a streamedaudio-visual program). The total view starts is, accordingly, one(“Total V.S.=1”) and is counted as a live view start (“Live V.S.=1”).

FIG. 4 is an example of a second use case for live Pausing. This usecase shows three viewing segments or media access segments, a first liveviewing segment 400 a from 6:00 pm-6:10 pm, a second DVR or time-shiftedviewing segment 400 b from 6:15 pm-6:30 pm and a third DVR ortime-shifted viewing segment 400 c from 6:45 pm-7:20 pm. The first timedelay between the first and second viewing segment 400 a, 400 b and thesecond time delay between the second and third viewing segment 400 b,400 c are each under the selected view threshold of 20 minutes (e.g., aduration of 5 minutes between the first and second viewing segments anda duration of 15 minutes between the second and third viewing segments,respectively). These three viewing segments 400 a-400 c are,accordingly, combined into a single view or media access session 402 ofP1E1.

In this example, the view start and all of the minutes for this view ormedia access session 402 are attributed to the DVR or time-shiftedclassification, as shown in the metric “DVR V.S.=1,” even though thefirst viewing segment 400 a from 6:00 pm-6:10 pm is live. This use casehighlights a convention advantageously used herein wherein the viewstart (V.S.) and all minutes of a view contribute to only one sourceclassification (Live, DVR or time-shifted, or VOD). In some examples,view starts and/or minutes for different viewing segments or mediaaccess segments (e.g., 400 a, 400 b, 400 c in the example of FIG. 4) arecombined, for a single view (e.g., 402 in the example of FIG. 4), to asingle source classification (e.g., “DVR V.S.” in the example of FIG. 4)based on the respective weights of the different viewing segments. Insome examples, if a view contains more live minutes than DVR ortime-shifted minutes, the entire view and view start is designated aslive. In some examples, if a view contains more DVR or time-shiftedminutes than live minutes, the entire view is designated as DVR ortime-shifted. In some examples, if a view contains equal live and DVR ortime-shifted minutes, the entire view is designated as live.Alternatively, in other examples, if a view contains equal live and DVRor time-shifted minutes, the entire view is designated as DVR ortime-shifted.

In accord with this convention, the addition of all source level viewstarts equals the total number of view starts overall and all minutesfor a view are classified collectively as one view start, which avoids ascenario where there are minutes without a view start. In the use caseof FIG. 4, the entire view or media access session 402 is designated asDVR or time-shifted viewing since the resulting view in this use casecontained more DVR or time-shifted minutes than live minutes.

FIG. 5 is an example of a third use case for a “breaking view” thresholdwherein a person watched P1E1 in three different video viewing segments:a first live viewing segment 500 a from 6:00 pm-6:05 pm (i.e., no playdelay), a second DVR or time-shifted viewing segment 500 b from 6:30pm-6:35 pm and a third DVR or time-shifted viewing segment 500 c from7:00 pm-7:05 pm. The actual time between viewing segments 500 a-500 band between viewing segments 500 b-500 c is 25 minutes, which exceedsthe set view threshold of 20 minutes. Accordingly, each of the viewingsegments 500 a-500 c is separately classified as a view, resulting in atotal of three views 502 a-502 c, with three view starts (“TotalV.S.=3”), classified as one live view start (“Live V.S.=1”) and two DVRor time-shifted view starts (“DVR V.S.=2”).

FIG. 6 is an example of a fourth use case for watching on delay. Thisexample is similar to the example of FIG. 3, except that the episode(P1E1) was watched on a 30-minute delay as denoted, for example, by the“30” beneath the beginning and ending of the P1E1 viewing segment 600.This results in the same total level metrics as the first use case ofFIG. 3 (“Total V.S.=1”), but the view start 602 and minutes (60 minutes)are attributed to DVR or time-shifted minutes at the source level (“DVRV.S.=1” and “DVR Minutes=60”).

FIG. 7 is an example of a fifth use case for fast-forward to live. Thisexample is similar to the use case of FIG. 4 except that, in thisexample, the person began the show on a delay (see, e.g., the “PlayDelay” of “10”) from 6:10 pm-6:30 pm, and then fast-forwarded to finishtheir viewing in correspondence with the live telecast from 6:45 pm-7:15pm (e.g., “Play Delay” of “0”). This use case shows three viewingsegments or media access segments 700, a first DVR or time-shiftedviewing segment 700 a from 6:10 pm-6:30 pm, a second DVR or time-shiftedviewing segment 700 b from about 6:32 pm-6:42 pm and a third liveviewing segment 700 c from about 6:44 pm-7:14 pm. The first time delaybetween the first and second viewing segment 700 a, 700 b and the secondtime delay between the second and third viewing segment 700 b, 700 c areeach under the selected view threshold of 20 minutes and these threeviewing segments 700 a-700 c are, accordingly, combined into a singleview or media access session 702 of P1E1. This is another example whereviewing segments of both live and DVR or time-shifted source arecombined into a single view 702. In this example, the total DVR ortime-shifted minutes (6:10 pm-6:30 pm and 6:32 pm-6:42 pm) are 30minutes and the total live minutes (6:44 pm-7:14 pm) are 30 minutes.Based on the convention described above in relation to FIG. 4, since theDVR or time-shifted and live minutes are equal, the entire view or mediaaccess session 702 is attributed to live in this example (“Live V.S.=1”and “Live Minutes=60”).

FIG. 8 is an example of a sixth use case for live versus DVR ortime-shifted. In FIG. 8, four viewing segments or media access segments800 a-800 d are shown. Viewing segment 800 a from 6:00 pm-6:10 pm islive (e.g., “Play Delay”=0), viewing segment 800 b from 6:15 pm-6:30 pmis DVR or time-shifted, viewing segment 800 c from 6:55 pm-7:15 pm islive (e.g., “Play Delay”=0 and viewing segment 800 d from 7:20 pm-7:30pm is DVR or time-shifted. Since the first two viewing segments 800a-800 b are separated by only 5 minutes, and the view threshold is setto 20 minutes, these first two viewing segments are combined into oneview or media access session 802 a. Here, the DVR or time-shiftedminutes (15 minutes from 6:15 pm-6:30 pm) are greater than the liveminutes (10 minutes from 6:00 pm-6:10 pm) in the first view, resultingin the entire view being considered DVR or time-shifted in accord withthe convention noted above (“DVR V.S.=1”). In the latter two viewingsegments 800 c-800 d of this example, the live minutes (20 minutes from6:55 pm-7:15 pm) are greater than the DVR or time-shifted minutes (10minutes from 7:20 pm-7:30 pm) and are separated in time by only 5minutes (less than the view threshold of 20 minutes), so these twoviewing segments 800 c-800 d are joined together and classified as oneview or media access session 802 b with the view being designated aslive (“Live V.S.=1”). Since the first set of two viewing segments 800a-800 b and the second set of two viewing segments 800 c-800 d areseparated by 25 minutes, with the view threshold being set to 20minutes, the first and second sets of viewing segments are treated astwo distinct views in this example.

FIG. 9 is an example of a seventh use case for live versus DVR ortime-shifted. In this example, six viewing segments or media accesssegments 900 a-900 f are combined into three distinct views 902 a-902 cseparated by 25 and 21 minutes, respectively (i.e., greater than theview threshold of 20 minutes). The first view 902 a (6:00 pm-6:05 pm) islive and consists of the viewing segment 900 a. The second view 902 bcomprises the viewing segments 900 b (Live—6:30 pm-6:35 pm), 900 c(DVR—6:37 pm-6:42 pm) and 900 d (DVR—6:45 pm-6:50 pm). The second view902 b is determined in this example to be DVR or time-shifted based onthe minutes weighting of the component segments (10 minutes of DVR vs. 5minutes of live). The third view 902 c, comprising viewing segment 900 e(DVR—7:11 pm-7:16 pm) and viewing segment 900 f (DVR—7:20 pm-7:30 pm),is all DVR or time-shifted viewing (see, e.g., “Play Delay” of “7” and“10” respectively). In view of the above, the total view starts arethree (“Total V.S.=3”) with one live view start (“Live V.S.=1”) and twoDVR or time-shifted view starts (“DVR V.S.=2”).

FIG. 10 is an example of an eighth use case for live versus DVR ortime-shifted. In this example, six viewing segments or media accesssegments 1000 a-1000 f are combined into three distinct views 1002a-1002 c separated by 23 and 26 minutes, respectively (i.e., greaterthan the view threshold of 20 minutes). The first view 1002 a comprisesthe viewing segments 1000 a (Live—6:00 pm-6:05 pm) and 1000 b (DVR—6:07pm-6:12 pm), which is designated to be live based on the occurrence ofequal parts live and DVR or time-shifted, although this convention couldbe optionally reversed and the view 1002 a designated as DVR ortime-shifted. The second view 1002 b comprises the viewing segments 1000c (Live—6:34 pm-6:39 pm) and 1000 d (DVR—6:45 pm-6:50 pm) and isclassified as live based on the occurrence of equal parts live and DVRor time-shifted. Again, this convention could be optionally reversedwith the view 1002 b having equal parts live and DVR or time-shiftedbeing designated as DVR or time-shifted. These viewing segments 1000c-1000 d are combined as the time between them is less than the viewthreshold of 20 minutes.

The third view 1002 c of FIG. 10 comprises the viewing segments 1000 e(Live—7:16 pm-7:21 pm) and 1000 f (DVR—7:25 pm-7:30 pm) and isclassified as live in this example based on the occurrence of equalparts live and DVR or time-shifted. These viewing segments 1000 e-1000 fare combined into view 1002 c as the time between them is less than theindicated view threshold of 20 minutes. In view of the above-describedviews 1002 a-1002 c, there are three view starts (“Total V.S.=3”)classified as three live view starts (“Live V.S.=3”).

FIGS. 11a-11b show an example of a ninth use case for Changing Channelsand represents a use where a person flips back and forth between twoprograms. When calculating view starts and minutes, the episodes aretreated separately. The three live viewing segments or media accesssegments 1100 a, 1100 c and 1100 e of the program P1E1 (6:00 pm-6:10 pm,6:20 pm-6:30 pm, and 6:40 pm-6:50 pm) all have 10 minute gaps betweenthem, which is under the given view threshold of 20 minutes, so they arecombined into a single P1E1 view 1102 a, as shown in the example of FIG.11a . Accordingly, for P1E1, the total view starts in this example isone (“Total V.S.=1”) and live view starts is one (“Live V.S.=1”). Theexample of FIG. 11b shows the same data as FIG. 11a , but highlights thethree viewing segments or media access segments 1100 b, 1100 d, 1100 fof program P2E3 (6:10 pm-6:20 pm, 6:30 pm-6:40 pm, and 6:50 pm-7:00 pm),which have 10 minute gaps therebetween and, under the given viewthreshold of 20 minutes, are combined and classified by the view counter154 as single P2E3 view 1102 b. For P2E3, the total view starts is one(“Total V.S.=1”) and live view starts is one (“Live V.S.=1”). Theduration of live minutes for each of P1E1 and P2E3 is 30 minutes.

FIG. 12 is an example of a tenth use case for “day boundary”. This usecase represents viewing of broadcast media across an arbitrary definedtime boundary (e.g., 6 am EST in the example of FIG. 12). In the exampleof FIG. 12, the media access session or viewing segment 1200 starts at5:30 am, continues past the arbitrary defined time boundary of 6 am, andends at 6:30 am. In this example, the view start is attributed to day 1,the interval prior to the boundary, whereas the minutes are attributedto the interval (e.g., day) on which the media is actually viewed. Asshown, a first portion (30 minutes) of the media access session orviewing segment 1200 is attributed to “day 1” and a second portion (30minutes) of the media access session or viewing segment 1200 isattributed to “day 2”. In this example, a unique audience is attributedto both days. Thus, when a media access session or viewing segment has aduration that extends beyond an arbitrary defined time boundary, aprogram or method in accord with at least some examples disclosed herein(e.g., program 1900 in FIG. 19) attributes a duration of a first portionof the media access session preceding the boundary to a first interval(e.g., day 1, etc.) and attributes a duration of a second portion of themedia access session following the boundary to a second interval (e.g.,day 2, etc.) following the boundary.

FIG. 13 shows a graph of a number of non-Internet based media viewstarts that result from a data set (Table 1) when different thresholdsbetween viewing periods are selected, such graph depicting an example ofa method that may be used to select a view threshold for viewing starts.In accord with the examples herein, any past, present and future methodsfor data analysis or statistical data analysis may be used tocharacterize the underlying data including, but not limited to, samplingstatistics (survey sampling and analysis), statistical models, curvefitting, line fitting, least squares, linear regression, generalizedlinear model (GLM), analysis of variance (ANOVA), correlation, or Bayes'theorem.

TABLE 1 #Viewing Starts/Threshold 0 1692171483 1 1646408645 2 16073861173 1574951814 4 1544503596 5 1519693348 6 1500928199 7 1485289747 81472115746 9 1462165652 10 1453834436 11 1446894362 12 1441138841 131436866587 14 1432761350 15 1429319021 16 1426080045 17 1423703238 181421327528 19 1418818501 20 1416786584 21 1414616698 22 1412780840 231410864749 24 1409122950 25 1407403263

In some examples, the threshold for determining non-Internet based mediaviewing Starts is determined by approximating a length of time betweenviewing periods that are perceived to define different viewingbehaviors. For example, FIG. 13 shows two trends (“Trend 1” and “Trend2”) based on aggregate viewer data. These trends are able to be used toidentify different viewing behaviors. As shown in FIG. 13, “Trend 1” istaken to represent a first behavior in which a viewer is reasonablylikely to return to a program that was previously viewed. Conversely,“Trend 2” of FIG. 13 is taken to represent a second behavior in which aviewer is relatively less likely to return to the program. Theintersection of FIG. 13's Trend 1 and Trend 2 itself represents a viewthreshold between viewing segments (media access segments), betweenabout 7-8 minutes, wherein viewer behavior is determined to generallytransition from a first behavior to a second behavior. In accordancewith this particular example data set, a view threshold between viewingsegments could then be set to be, for example, 7 minutes or 8 minutes,and viewing segments for media occurring beyond that view thresholdcould be considered different views and different view starts consistentwith the teachings herein.

In another example, shown in the graph of FIG. 14, which is drawn fromthe data in Table 2, below, a view threshold is determined usingmeasured differences in non-Internet based media viewing starts betweenone interval to the next, such as the difference in non-Internet basedmedia viewing starts between 0 minutes and 1 minute, the difference innon-Internet based media viewing starts between 1 minute and 2 minutes,etcetera.

TABLE 2 Difference in viewing starts Interval Minutes from priorinterval 1 45762838 2 39022528 3 32434303 4 30448218 5 24810248 618765149 7 15638452 8 13174001 9 9950094 10 8331216 11 6940074 125755521 13 4272254 14 4105237 15 3442329 16 3238976 17 2376807 182375710 19 2509027 20 2031917 21 2169886 22 1835858 23 1916091 241741799 25 1719687

In yet another example, shown in the graph of FIG. 15, which is drawnfrom the data in Table 3, below, a view threshold is determined via thepercentage difference in non-Internet based media viewing starts betweenone interval to the next in Table 1 and FIG. 13, such as the percentagedifference in non-Internet based media viewing starts between 0 minutesand 1 minute, the percentage difference in non-Internet based mediaviewing starts between 1 minute and 2 minutes, etcetera.

TABLE 3 PCT change in Viewing Starts Interval Minutes from priorinterval 1 2.7043854 2 2.3701605 3 2.0178290 4 1.9332793 5 1.6063574 61.2347984 7 1.0419187 8 0.8869651 9 0.6759043 10 0.5697861 11 0.477363412 0.3977845 13 0.2964499 14 0.2857076 15 0.2402584 16 0.2266097 170.1666672 18 0.1668683 19 0.1765270 20 0.1432119 21 0.1531555 220.1297778 23 0.1356255 24 0.1234561 25 0.1220395

Flowcharts representative of example machine readable instructions whichmay be executed to implement the example watermark based mediaimpression handler 150 of FIGS. 1-2 are shown in FIGS. 16-18. In theseexamples of FIGS. 16-18, the machine readable instructions comprise aprogram for execution by a processor such as the processor 612 shown inthe example processor platform 600 discussed below in connection withFIG. 20. The program may be embodied in software stored on a tangiblecomputer readable storage medium such as a CD-ROM, a floppy disk, a harddrive, a digital versatile disk (DVD), a Blu-ray disk, or a memoryassociated with the processor 612, but the entire program and/or partsthereof could alternatively be executed by a device other than theprocessor 612 and/or embodied in firmware or dedicated hardware.Further, although the example program is described with reference to theflowcharts illustrated in FIGS. 16-18, many other methods ofimplementing the example watermark based media impression handler 150and/or the view counter 154 may alternatively be used. For example, theorder of execution of the blocks may be changed, and/or some of theblocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 16-18, or otherprocesses disclosed herein, may be implemented using coded instructions(e.g., computer and/or machine readable instructions) stored on atangible computer readable storage medium such as a hard disk drive, aflash memory, a read-only memory (ROM), a compact disk (CD), a digitalversatile disk (DVD), a cache, a random-access memory (RAM) and/or anyother storage device or storage disk in which information is stored forany duration (e.g., for extended time periods, permanently, for briefinstances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term tangible computer readablestorage medium is expressly defined to include any type of computerreadable storage device and/or storage disk and to exclude propagatingsignals and to exclude transmission media. As used herein, “tangiblecomputer readable storage medium” and “tangible machine readable storagemedium” are used interchangeably. Additionally or alternatively, theexample processes of FIGS. 16-18, or other processes disclosed herein,may be implemented using coded instructions (e.g., computer and/ormachine readable instructions) stored on a non-transitory computerand/or machine readable medium such as a hard disk drive, a flashmemory, a read-only memory, a compact disk, a digital versatile disk, acache, a random-access memory and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm non-transitory computer readable medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, when the phrase “at least” is used as the transition termin a preamble of a claim, it is open-ended in the same manner as theterm “comprising” is open ended.

FIG. 16 is a flowchart representative of example machine languageinstructions 1600 that may be executed to implement the examplewatermark based media impression handler 150 of FIGS. 1-2 to generateview metrics in accordance with the teachings herein. In the exampleprogram 1600, the meter 108 b of the example media presentationenvironment 102 b, shown in FIG. 1, is adapted to receive watermarkedmedia (e.g., media having a sequence of bits or data, an audio code,etc. inserted periodically into an audio stream) via the terrestrialmedia provider 120A and generate watermark based impression recordscorresponding to the media accessed or viewed by the panelist(s) 101 b(Block 1605). For example, in FIG. 3, the watermarked media isidentified by the meter 108 b, via the watermarks, to correspond tomedia P1E1. Further, the watermarks may bear encoded informationrelating to source identification to each content provider ordistribution source so as to uniquely identify, for example, thedistribution source. Consistent with FIG. 1, these watermark basedimpression records corresponding to media viewed by the panelist(s) 101b over an interval of interest are exported to the central facility 104(Block 1605) where they are processed by the example watermark basedmedia impression handler 150.

FIG. 17 is a flowchart representative of example machine readableinstructions 1700 that may be executed to generate Internet basedimpression records (Block 1705) utilizable in combination with theexample watermark based media impression handler 150 of FIGS. 1-2 togenerate Total Content Ratings (TCR) utilizing a single set of metricsfor video content ratings across all platforms where the content isconsumed. Internet based media is not watermarked in the manner in whichterrestrial broadcast television is watermarked. Instead, in the exampleprogram 1700 of FIG. 17, the meter 108 a of the example mediapresentation environment 102 a is adapted to receive characteristic data(e.g., HTTP data, meta tags, metadata, etc.) identifying the media. Themetadata includes, for example, the purpose of the data, time and dateof creation of the data, copyright information, creator information,keywords, location on a computer network where the data was created,standards used, file size, media format, etcetera. Unlike terrestrialmedia wherein the watermarks are contained within the media itself,which allows precise determination of which MOP is being viewed, theInternet based media is stored separately than its correspondingmetadata. Moreover, such audio typically does not include timestampsthrough the audio stream, which prevents collection of specific MOPs ofthe measured media. The meter 108A may further comprise anactive/passive meter (A/P) configured to passively identify the media,by signature/fingerprint, should the media fail to be identified viaactive (coding) techniques (e.g., media lacking identifiable watermarks,etc.). The passive identification includes, for example, sampling ofaudio streams using the meter 108 a and comparing the samples (orcharacterizations of the samples—sometimes referred to as signatures) toone or more databases (e.g., a central database in the central facility104, etc.) for a match to identified media. For example, were the mediato be viewed via the Internet, and the watermarks identifying the media(e.g., as P1E1) were unavailable for any reason, the program can beidentified passively using one or more audio and/or video systems. Themeter 108 a exports the internet based impression records, correspondingto the Internet-based media accessed or viewed by the panelist(s) 101 aover an interval of interest, to the central facility 104 (Block 1710).

FIG. 18 is a flowchart representative of example machine readableinstructions 1800 which may be executed, via the example watermark basedmedia impression handler 150 of FIG. 1, which includes the examplerecord locator 152, the example view counter 154 and the example mediacreditor 156, to compare a television provider audience to an Internetbased audience to generate Total Content Ratings (TCR) utilizing asingle set of metrics for video content ratings across all platformswhere the content is consumed.

The example program 1800 begins when the example viewing segmentcollector 213 of the example view counter 154 obtains impression recordsfor a panelist (Block 1805) selected from the population of panelists101 a-101 n from the example media presentation environments 102A-102N,wherein N is any integer. The example viewing segment collector 213determines whether the impression records obtained from the panelistcorrespond to watermarked media (Block 1810).

For purposes of illustration, the description of FIG. 18 is supplementedbelow by an example data set of a population of 6 example panelists,wherein TABLE 4 corresponds to non-Internet based media (e.g., TV datacollection or television provider audience impression records collectedvia example meters 108 a-108 n of FIG. 1, etc.) and TABLE 5 correspondsto Internet based media (e.g., digital viewing data collected viaexample meters 108 a-108 n of FIG. 1, or other meters, sources ordigital devices including computers, smartphones, tablets, portablemedia players and connected devices and corresponding data (native orweighted) provided thereby (e.g., via software developer kit (SDK) basedtools, CMS tags, ID3 tags, etc.) and/or data provided through thirdparty providers.

TABLE 4 (non-Internet based media) Person ID Originator Program StartTime End Time P1 CBS BBT 9:00 9:10 P1 CBS BBT 9:12 9:22 P2 CBS BBT 9:009:20

TABLE 5 (Internet based media) Person ID Originator Program Start TimeEnd Time P2 CBS BBT 9:00 9:20 P2 CBS BBT 9:22 9:30 P3 CBS BBT 9:12 9:22

If a watermark is identified (Block 1810 returns a result of YES), theexample viewing segment sorter 214 identifies the media corresponding tothe watermark. In the example provided above in TABLES 4-5, the mediacorresponding to the watermark is determined to be the CBS program “TheBig Bang Theory” (BBT)). Following this identification of the mediacorresponding to the watermark (e.g., television media) by the exampleviewing segment sorter 214, the example the example viewing segmentclassifier 215 converts the watermark based impression records to mediacompatible impression records (Block 1825) corresponding to anothermedia (e.g., Internet), such as is shown in FIG. 19, discussed below. Inone example, this is accomplished by collecting all viewing segments ofa specific piece of content for a particular panelist for a relevantperiod of measurement using the viewing segment collector 213 andchronologically sorting the viewing segments using the viewing segmentsorter 214 (e.g., sorting the viewing segments 700 a-700 c in FIG. 7 formedia P1E1), followed by conversion of the watermark based impressionrecords to media compatible impression records by the example viewingsegment classifier 215.

With respect to the example of TABLES 4-5, above, the viewing segmentcollector 213 collects all viewing segments of a specific piece ofcontent (e.g., BBT) for a particular panelist (e.g., P1) for a relevantperiod of measurement (e.g., BBT, broadcast between 9:00-9:30 pm on Day1). The viewing segment sorter 214 chronologically sorts the viewingsegments by collecting the first viewing segment for P1 starting at 9:00and ending at 9:10 and collecting the second viewing segment starting at9:12 and ending at 9:22 (20 minutes with no break), indicating thatpanelist P1 either paused the media or moved to a different mediacontent for 2 minutes before resuming watching the same media (BBT) foranother 10 minutes.

Then, the example viewing segment classifier 215 combines the viewingsegments when the time between the segments is less than a determinedviewing threshold and classifies such combined viewing segments as oneview (e.g., combining viewing segments 700 a-700 c in FIG. 7 into oneview). In this example, the example view start designator 216 designatesone view start and resulting duration to each view. With respect to theexample of panelist P1 in TABLES 4-5, in relation to the flowchart 1900of FIG. 19 and in accordance with an example viewing threshold of 20minutes, the first and second viewing segments would be combined andclassified as one view.

In one example, the example view counter 154 then determines if theimpression records converted in Block 1825 represent the last panelistin a population of N panelists (Block 1835). If the panelist is not thelast panelist in a population of N panelists (Block 1835 returns aresult of NO), the example view counter 154 returns control to Block1805 for further processing. In the example provided above in TABLES4-5, following treatment of the first and second impression records ofpanelist P1 by the example viewing segment collector 213, the exampleviewing segment sorter 214, the example viewing segment classifier 215,and the example view start designator 216, Block 1835 would return aresult of NO and control would return to Block 1805 for furtherprocessing of the impression records of panelist P2, includingcollecting the first viewing segment for P2 starting at 9:00 and endingat 9:20.

If the impression records for a panelist are not watermark based (Block1810 returns a result of NO), the impression records corresponds toInternet based audience impression records, the example Internet-basedmedia impression handler 151 identifies the media (e.g., a time-shiftedstreaming of a particular BBT episode) corresponding to the Internetbased impression records (Block 1820) using conventional techniques forevaluating Internet-based media impressions. In the example providedabove in TABLES 4-5, following treatment of the first impression recordof panelist P2, the non-Internet based first viewing segment, theexample Internet-based media impression handler 151 processes the secondimpression record of Block 1805, an Internet based impression recordwith a viewing segment for P2 starting at 9:22 and ending at 9:30 (e.g.,following the initial view of 20 minutes, the panelist P2 re-loaded theweb page or app for 2 minutes before resuming watching the same programfor another 8 minutes), using conventional techniques for evaluatingInternet-based media impressions. Then, the example program 1800proceeds to recursively process the third Internet based impressionrecord, a viewing segment for P3 starting at 9:12 and ending at 9:20 (10minutes without breaking). The Internet based impression records (Block1830) are evaluated by the example Internet-based media impressionhandler 151, using conventional techniques for evaluating Internet-basedmedia impressions, to determine if the Internet based impression recordsprocessed in Block 1830 represent the last panelist in a population of Npanelists (Block 1840). If the panelist is not the last panelist in apopulation of N panelists (Block 1840 returns a result of NO), controlreturns to Block 1805 for further processing by the exampleInternet-based media impression handler 151 using conventionaltechniques for evaluating Internet-based media impressions. In theexample provided above in TABLES 4-5, following treatment of the thirdimpression record of panelist P2, the Internet based third viewingsegment, Block 1840 returns a result of NO and the control returns toBlock 1805 for further processing, wherein the example Internet-basedmedia impression handler 151 collects the impression record associatedwith panelist P3, an Internet based impression record with a viewingsegment starting at 9:12 and ending at 9:22.

Following processing of all impression records for all N panelists, inBlock 1860, the example media creditor 156 uses the impression recordsfrom Block 1845 and Block 1850 to respectively generate televisionaudience measurement metrics using the Internet-based media compatibleimpression records from Block 1825 and to generate internet basedaudience measurement metrics by comparing and/or unifying the impressionrecords from the television audience and the Internet audience todetermine audience metrics for the populations of panelists in bothmedia platforms (e.g., television, Internet, etc.), or in sub-portionsthereof.

By way of example, with reference to the above-noted example providedabove in TABLES 4-5, a broadcast of a particular Big Bang Theory episodeand time-shifted streaming of that particular Big Bang Theory episode,direct comparisons of the viewing data is performed (e.g., comparison ofAverage Audiences (“AA”), determined in some examples as total viewedduration/(media length*universe estimate), where the universe estimateis the total persons or homes in a given population). As anotherexample, Average Minute Audience (“AMA”) for an average number ofindividuals or homes or other target group viewing a particular media inone or more platforms can be calculated across both non-Internet basedmedia and Internet based media.

With reference to the example of TABLES 4-5, above, Table 6 showsexample metrics for the non-Internet based media and TABLE 7 showsmetrics for the Internet based media.

TABLE 6 (non-Internet based media) Time Unique Media AA OriginatorProgram Views Spent Audience Length Projection CBS BBT 2 40 2 20 2

TABLE 7 (non-Internet based media) Time Unique Media AA OriginatorProgram Views Spent Audience Length Projection CBS BBT 3 38 2 30 1.27

In TABLE 6, the non-Internet based media of TABLE 1 shows that, betweenpanelists P1 and P2, the total number of views was 2, the time spent ortotal viewed duration was 40 minutes, and the unique audience(unduplicated count of persons) was 2. The media length represents thenumber of minutes of actual program content aired which, for thisexample, is 20 minutes. The Average Audience (“AA”) Projection isderived by dividing the time spent or total viewed duration by the medialength, here 40/20=2.

In TABLE 7, the Internet based media of Table 2 shows that, betweenpanelists P2 and P3, the total number of views (the number of times themedia began playing) was 3, the time spent or total viewed duration was38 minutes, and the unique audience (unduplicated count of persons) was3. As to the views, for Internet-based media, any time digital media isstarted (or re-started), it is deemed to be a new view, which results inthe example above with 3 views. The media length represents the numberof minutes of actual program content aired which, for this example, is30 minutes. The Average Audience (“AA”) Projection is derived bydividing the time spent or total viewed duration by the media length,here 38/30=1.27. It is noted that a panelist could watch the same mediaover again on a digital device and this could account for a total viewedduration value that is longer than the actual media length.Additionally, media length for the same program can vary acrossnon-Internet based media and Internet based media, as represented by thedifferent media lengths in TABLES 6-7, because media providers mayprovide different versions of the same media (e.g., a regular version,an extended version, etc.).

Certain metrics, like total viewed duration and views are combinable andare simply summed up across non-Internet based media and Internet basedmedia. For example, the derived data of TABLES 6-7 show that for the BBTnon-Internet based media and Internet based media represented, the totalviewed duration between the three panelists P1-P3 was 78 minutes and thetotal views was 5, as shown below in TABLE 8.

TABLE 8 (Combined Internet-based and non-Internet based media) TimeUnique Media AA Originator Program Views Spent Audience LengthProjection CBS BBT 5 78 3 24.87 3.14

The media length is calculated, in this example, an ((non-Internet basedmedia time spent*non-Internet based media media length)+(Internet basedmedia time spent*Internet based media media length))/(non-Internet basedmedia time spent+Internet based media time spent), yielding a medialength of 24.87. The AA projection is 3.14, when the time spent (78minutes) is divided by the derived media length (24.87 minutes).

Reach metrics provide unduplicated audience estimate for various marketbreaks and demographics. By way of example, reach metrics can represent(1) in non-Internet based media ratings, an unduplicated number ofindividuals or households exposed to an advertising medium at least onceduring the average week for a reported time period or (2) in Internetbased media usage, the percentage of U.S. Internet users that accessedthe Web media of a specific site or property. Reach metrics cannot bedirectly summed up across non-Internet based media and Internet basedmedia prior to accounting for potential duplication (e.g., the sameviewer accessing the same media across both non-Internet based media andInternet based media). The unique audience metric provides weightingthat accounts for duplication. Since the panelist P2 in this exampleconsumed the same media on both non-Internet based media and Internetbased media devices, the panelist P2 is only counted once when reportingthe total audience. For the total audience, media length is derived byduration weighting the different lengths across non-Internet based mediaand Internet based media, with the AA Projection being derived bydividing the total viewing time (“Time Spent”) by the duration weightedmedia length.

As to Block 1860 of the example program of FIG. 18, not only can theimpression records from the non-Internet based audience and the Internetbased audience be combined, but they can also, or alternatively, becompared. For instance, in the example of Tables 4-8, the number ofviews and the total viewed duration of the Internet based audience isgreater than that of the non-Internet based audience.

In some examples, a total viewed duration for non-Internet based mediais able to be measured with granularity as to a number of seconds ofmedia exposure associated with a particular media or entity for allviewers across a measurement period, and the resulting Average Audiencecalculation for non-Internet based media (measured in seconds) is thencombinable with or reconcilable with corresponding Average Audiencecalculations performed for Internet-based media, thus enablingcalculation of metrics for view of particular media in one or moreplatforms across both non-Internet based media and Internet based media.

The viewing data of example Tables 1-2, and derived data of Tables 3-4can further be combined to yield metrics such as an Average MinutesAudience (“AMA”), determined in one example as (total viewedduration)/(media length). For example, in this example of Tables 1-4,the total viewed duration is 78 minutes and the media length is weightedat 24.87 minutes, providing an AA of 78/24.87 or 3.14, meaning that forany given minute of the program, on average, about 3.14 people werewatching.

FIG. 19 is a flowchart 1900 representative of example machine readableinstructions which may be executed to implement block 1825 of FIG. 18 toconvert watermark based impression records (e.g., broadcast television)to Internet-based media compatible impression records. In Block 1905,the example watermark based media impression handler 150, whereverlocated (e.g., centralized in central facility 104 as shown in FIG. 1,or distributed) collects all viewing segments of media (e.g., viewingevents that come from a meter 108 a, etc.) for a particular panelist,via the example view counter 154 and the example viewing segmentcollector 213. These collected viewing segments or media access segments(e.g., 400 a-400 c in FIG. 4) are sorted by the viewing segment sorter214 of the example view counter 154 (e.g., chronologically sorted, etc.)for a particular panelist based on time (Block 1910). The example viewcounter 154 of the example watermark based media impression handler 150then determines if an amount of time between adjacent viewing segmentsis less than a view threshold using the example viewing segmentclassifier 215 (Block 1915). If an amount of time between adjacentviewing segments is less than and/or equal to a view threshold, theadjacent viewing segments are combined into a single view by the exampleviewing segment classifier 215 (Block 1920). If an amount of timebetween adjacent viewing segments is greater than a view threshold, theadjacent viewing segments are classified as different views by theexample viewing segment classifier 215 (Block 1925), such as isrepresented in the examples of FIGS. 3-12. Following this classificationof the viewing segments into views by the example viewing segmentclassifier 215, the example view counter 154 example view startdesignator 216 attributes one view start, and a duration correspondingto the view start, to each classified view (Block 1930).

The example watermark based media impression handler 150 then determinesif a last media has been processed in Block 1935. If Block 1935 returnsa result of NO, control returns to Block 1905 where the viewing segmentcollector 213 collects viewing segments of the next media for thepanelist, followed by sorting of the next media by viewing segmentsorter 214 in Block 1910, classification of the next media by viewingsegment classifier 215 in Blocks 1915, 1920 and 1925, and attribution ofone video start and the resulting duration minutes to each view by theview start designator 216 in Block 1930. The example watermark basedmedia impression handler 150 continues such processing of the mediauntil the Block 1935 returns a result of YES, at which point control ispassed to Block 1835 for continued processing of all viewing segmentsfrom a next panelist and/or all successive panelists in a population bythe example watermark based media impression handler 150.

FIG. 20 is a block diagram of an example processor platform 2000 capableof executing the instructions of FIGS. 16-18 to implement the examplewatermark based media impression handler 150 of FIGS. 1-2. In variousexamples, the processor platform 2000 is, by way of example, a server, adesktop computer, a laptop computer, or a mobile device (e.g., a cellphone, a smart phone, a tablet such as an iPad™), or any other type ofcomputing device.

The processor platform 2000 of the illustrated example includes aprocessor 2012. The processor 2012 of the illustrated example ishardware. For example, the processor 2012 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 2012 of the illustrated example includes a local memory2013 (e.g., a cache). The processor 2012 executes instructions toimplement the example watermark based media impression handler 150, theexample view counter 154, the example viewing segment collector 213, theexample viewing segment sorter 214, the example viewing segmentclassifier 215, and the example viewing start designator 216 of FIG. 2,or other examples expressly or implicitly disclosed herein. Theprocessor 2012 of the illustrated example is in communication with amain memory including a volatile memory 2014 and a non-volatile memory2016 via a bus 2018. The volatile memory 2014 may be implemented bySynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or anyother type of random access memory device. The non-volatile memory 2016may be implemented by flash memory and/or any other desired type ofmemory device. Access to the main memory 2014, 2016 is controlled by amemory controller.

The processor platform 2000 of the illustrated example also includes aninterface circuit 2020. The interface circuit 2020 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 2022 are connectedto the interface circuit 2020. The input device(s) 2022 permit(s) a userto enter data and commands into the processor 2012. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system. Inthe illustrated example of FIG. 17, the example input devices(s)implement inputs to, for example, the example meter 108 and/or device(s)105 operatively associated therewith.

One or more output devices 2024 are also connected to the interfacecircuit 2020 of the illustrated example. The output devices 2024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer, speakers, etc.). The interface circuit 2020 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 2020 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network2026 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 2000 of the illustrated example also includes oneor more mass storage devices 2028 for storing software and/or data.Examples of such mass storage devices 2028 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 2032 of FIGS. 16-18 may be stored in the massstorage device 2028, in the local memory 2013, in the volatile memory2014, in the non-volatile memory 2016, and/or on a removable tangiblecomputer readable storage medium such as a CD or DVD.

From at least the foregoing, it will be appreciated that examplemethods, apparatus and articles of manufacture disclosed herein providea “view” metric for the purposes of measuring non-Internet based mediaaudiences in a manner to facilitate cross-platform ratings. Examplesdisclosed herein determine the view threshold such that the definitionof a non-Internet based media view start is similar to an Internet basedmedia view start to enable views to be used for both non-Internet basedand Internet based media to, in turn, enable a Total Content Ratings(TCR) utilizing a single set of metrics across all media distributionplatforms.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus to generate audience metrics acrossdifferent media platforms, comprising: a segment collector to: accessimpression records indicative of media access segments, the media accesssegments including start times and end times corresponding to mediaaccessed by a panelist; and determine ones of the impression recordsthat include a watermark corresponding to a first media platformpresenting the media; a segment classifier to: convert a first one ofthe impression records including the watermark to a converted impressionrecord; and combine the converted impression record corresponding to thefirst media platform and a second impression record corresponding to asecond media platform; and a media creditor to generate audiencemeasurement metrics based on the combined impression records.
 2. Theapparatus of claim 1, further including a viewing segment sorter toarrange the ones of the impression records including the watermark in achronological order to identify adjacent ones of the impression records,the chronological order based on times the impression records wereattributed to the panelist.
 3. The apparatus of claim 1, wherein thesegment classifier is to determine a time period separating adjacentfirst and second ones of the media access segments of the ones of theimpression records including the watermark, the time period defined byan end time of the first one of the media access segments and a starttime of the second one of the media access segments.
 4. The apparatus ofclaim 3, wherein the time period defines a duration during which themedia was not presented via the first media platform between the firstone of the media access segments and the second one of the media accesssegments.
 5. The apparatus of claim 3, wherein the segment classifier isto determine if the time period exceeds a threshold, the thresholdcorresponding to a maximum duration between adjacent media accesssegments for the adjacent media access segments to be considered a partof a same media access session.
 6. The apparatus of claim 1, wherein thesegment classifier is to convert the ones of the impression recordsincluding the watermark into views, the views determined based on athreshold corresponding to a maximum duration between adjacent mediaaccess segments.
 7. The apparatus of claim 6, wherein the ones of theimpression records including the watermark converted into views are of asame format as the impression records corresponding to the second mediaplatform.
 8. A method to generate audience metrics across differentmedia platforms, comprising: determining, using a processor, ones ofimpression records that include a watermark corresponding to a firstmedia platform; converting, using the processor, a first one of theimpression records including the watermark to a converted impressionrecord; combining, using the processor, the converted impression recordcorresponding to the first media platform and a second impression recordcorresponding to a second media platform; and generating, using theprocessor, audience measurement metrics based on the combined impressionrecords.
 9. The method of claim 8, further including arranging the onesof the impression records including the watermark in a chronologicalorder to identify adjacent ones of the impression records, thechronological order based on times the impression records wereattributed to a panelist.
 10. The method of claim 8, further includingdetermining a time period separating adjacent first and second ones ofmedia access segments of the ones of the impression records includingthe watermark, the time period defined by an end time of the first oneof the media access segments and a start time of the second one of themedia access segments.
 11. The method of claim 10, wherein the timeperiod defines a duration during which the media was not presented viathe first media platform between the first one of the media accesssegments and the second one of the media access segments.
 12. The methodof claim 10, further including determining if the time period exceeds athreshold, the threshold corresponding to a maximum duration betweenadjacent media access segments for the adjacent media access segments tobe considered a part of a same media access session.
 13. The method ofclaim 8, further including converting the ones of the impression recordsincluding the watermark into views, the views determined based on athreshold corresponding to a maximum duration between adjacent mediaaccess segments.
 14. The method of claim 13, wherein the ones of theimpression records including the watermark converted into views are of asame format as the impression records corresponding to the second mediaplatform.
 15. A non-transitory computer readable storage mediumcomprising instructions that, when executed, cause a machine to atleast: determine ones of impression records that include a watermarkcorresponding to a first media platform; convert a first one of theimpression records including the watermark to a converted impressionrecord; combine the converted impression record corresponding to thefirst media platform and a second impression record corresponding to asecond media platform; and generate audience measurement metrics basedon the combined impression records.
 16. The non-transitory computerreadable medium of claim 15, wherein the instructions further cause themachine to arrange the ones of the impression records including thewatermark in a chronological order to identify adjacent ones of theimpression records, the chronological order based on times theimpression records were attributed to a panelist.
 17. The non-transitorycomputer readable medium of claim 15, wherein the instructions furthercause the machine to determine a time period separating adjacent firstand second ones of media access segments of the ones of the impressionrecords including the watermark, the time period defined by an end timeof the first one of the media access segments and a start time of thesecond one of the media access segments.
 18. The non-transitory computerreadable medium of claim 17, wherein the instructions further cause themachine to define the time period as a duration during which the mediawas not presented via the first media platform between the first one ofthe media access segments and the second one of the media accesssegments.
 19. The non-transitory computer readable medium of claim 17,wherein the instructions further cause the machine to determine if thetime period exceeds a threshold, the threshold corresponding to amaximum duration between adjacent media access segments for the adjacentmedia access segments to be considered a part of a same media accesssession.
 20. The non-transitory computer readable medium of claim 15,wherein the instructions further cause the machine to convert the onesof the impression records including the watermark into views, the viewsdetermined based on a threshold corresponding to a maximum durationbetween adjacent media access segments.